Data processing method, category identification method and computer device

Abstract

The present application provides a data processing method, a category identification method and a computer device, in which, firstly, node information is screened out based on node edge data and feature information, that is, K first nodes with rich structures and high feature discrimination are located, and then a feature similarity is calculated based on relevant information of the first nodes. On the other hand, since a calculation amount of the feature similarity of the node is relatively small, for a case that model training and prediction of node category are completed at the same time, a second node can include a node to be classified, and a model parameter used in category prediction is obtained by performing parameter iterative updating with taking the node to be classified as an unlabeled node.

What is claimed is: 
     
         1 . A data processing method, the method comprises:
 determining node information of a first node based on edge data and feature information of a second node of graph data; wherein the second node comprises a labeled node and the first node;   calculating feature similarity between the second node and a neighborhood node of the second node based on the node information of the first node; aggregating feature information of the second node based on the feature similarity and feature information of the neighborhood node;   determining a prediction result based on the aggregated feature information, wherein the prediction result comprises a predicted category label of the labeled node; and   iteratively updating a parameter of a classification model based on the predicted category label of the labeled node and a real category label of the labeled node.   
     
     
         2 . The method according to  claim 1 , wherein the determining the node information of the first node based on the edge data and the feature information of the second node of the graph data comprises:
 determining a first score of the second node based on the edge data between the second node and the neighborhood node of the second node;   determining feature information of a third node based on the first score and the feature information of the second node; wherein the third node is selected from the second node;   determining a second score of the third node based on the feature information of the third node; and   determining the node information of the first node based on the second score.   
     
     
         3 . The method according to  claim 2 , wherein the determining the first score of the second node based on the edge data between the second node and the neighborhood node of the second node comprises:
 determining a first matrix based on an adjacency matrix of the graph data and a first reference matrix of the graph data; wherein the adjacency matrix is obtained based on the edge data, the first reference matrix of the graph data is a column matrix comprising a plurality of preset values; and   determining the first score of the second node based on the first matrix.   
     
     
         4 . The method according to  claim 2 , wherein the determining the feature information of the third node based on the first score and the feature information of the second node comprises:
 determining a fifth matrix based on an initial matrix of the graph data and the third node; wherein the initial matrix is obtained based on the feature information of the second node, and the fifth matrix is a matrix comprising the feature information of the third node; and   determining the feature information of the third node based on the fifth matrix.   
     
     
         5 . The method according to  claim 4 , wherein the determining the second score of the third node based on the feature information of the third node comprises:
 determining a second matrix based on the fifth matrix, wherein the second matrix is a column matrix comprising the second scoreof the third node.   
     
     
         6 . The method according to  claim 5 , wherein the determining the second score of the third node based on the feature information of the third node further comprises:
 determining an eighth matrix based on the second matrix, wherein the eighth matrix is a column matrix comprising a difference between the second score of the third node and an average score;   determining a third matrix based on the second matrix and a second reference matrix, wherein the second reference matrix is determined based on the eighth matrix; and   determining the secondscore of the third node based on the third matrix.   
     
     
         7 . The method according to  claim 2 , wherein the determining the node information of the first node based on the second score comprises:
 determining a sixth matrix based on the first node; wherein the sixth matrix comprises any one of a seventh matrix and a fourth matrix, the seventh matrix is a matrix comprising feature information of the first node, and the fourth matrix is a matrix comprising a second score of the first node; and   determining the node information of the first node based on the sixth matrix.   
     
     
         8 . The method according to  claim 1 , wherein the second node further comprises an unlabeled node, and the unlabeled node comprises a node to be classified, and the prediction result further comprises a predicted category label of the unlabeled node; wherein the method further comprises:
 determining a predicted category label of the node to be classified based on the prediction result output by a last round of training of the classification model.   
     
     
         9 . The method according to  claim 1 , further comprising:
 acquiring the graph data corresponding to a classification task; wherein, in a condition that the classification task is a task for classifying articles, graph data of the task for classifying articles is acquired, wherein the second node comprises an article node;   in a condition that the classification task is a task for classifying risky users, graph data of the task for classifying risky users is acquired, wherein the second node comprises a user node; and   in a condition that the classification task is a task for classifying pushed users, graph data of the task for classifying pushed users is acquired, wherein the second node comprises the user node.   
     
     
         10 . The method according to  claim 9 , wherein the feature information of the second node comprises feature information of an article or a user, and the edge data between the second nodes comprises a connection edge between two second nodes, and the connection edge indicates that there is a preset association relationship between two articles or between two users. 
     
     
         11 . A category identification method, the method comprises:
 determining node information of a first node based on graph data, wherein the graph data comprises a node to be classified and a sampled node, and the node to be classified and the sampled node comprise the first node;   calculating feature similarity between the node to be classified and a neighborhood node of the node to be classified based on the node information of the first node, aggregating feature information of the node to be classified based on the feature similarity and feature information of the neighborhood node; and   determining predicted category information of the node to be classified based on the aggregated feature information.   
     
     
         12 . The method according to  claim 11 , further comprising:
 acquiring the graph data corresponding to a classification task; wherein, in a condition that the classification task is a task for classifying articles, graph data of the task for classifying articles is acquired, wherein both the node to be classified and the sampled node comprise an article node;   in a condition that the classification task is a task for classifying risky users, graph data of the task for classifying risky users is acquired, wherein both the node to be classified and the sampled node comprises a user node; and   in a condition that the classification task is a task for classifying pushed users, graph data of the task for classifying pushed users is acquired, wherein both the node to be classified and the sampled node comprises the user node.   
     
     
         13 . A computer device, comprising:
 a processor; and   a memory arranged to store computer-executable instructions, the processor, when executing the computer-executable instructions, is configured to:   determine node information of a first node based on edge data and feature information of a second node of graph data; wherein the second node comprises a labeled node and the first node;   calculate feature similarity between the second node and a neighborhood node of the second node based on the node information of the first node; aggregate feature information of the second node based on the feature similarity and feature information of the neighborhood node;   determine a prediction result based on the aggregated feature information, wherein the prediction result comprises a predicted category label of the labeled node; and   iteratively update a parameter of a classification model based on the predicted category label of the labeled node and a real category label of the labeled node.   
     
     
         14 . The computer device according to  claim 13 , wherein the processor is configured to:
 determine a first score of the second node based on the edge data between the second node and the neighborhood node of the second node;   determine feature information of a third node based on the first score and the feature information of the second node; wherein the third node is selected from the second node;   determine a second score of the third node based on the feature information of the third node; and   determine the node information of the first node based on the second score.   
     
     
         15 . The computer device according to  claim 14 , wherein the processor is configured to:
 determine a first matrix based on an adjacency matrix of the graph data and a first reference matrix of the graph data; wherein the adjacency matrix is obtained based on the edge data, the first reference matrix of the graph data is a column matrix comprising a plurality of preset values; and   determine the first score of the second node based on the first matrix.   
     
     
         16 . The computer device according to  claim 14 , wherein the processor is configured to:
 determine a fifth matrix based on an initial matrix of the graph data and the third node; wherein the initial matrix is obtained based on the feature information of the second node, and the fifth matrix is a matrix comprising the feature information of the third node; and   determine the feature information of the third node based on the fifth matrix.   
     
     
         17 . The computer device according to  claim 16 , wherein the processor is configured to:
 determine a second matrix based on the fifth matrix, wherein the second matrix is a column matrix comprising the second scoreof the third node.   
     
     
         18 . The computer device according to  claim 17 , wherein the processor is configured to:
 determine an eighth matrix based on the second matrix, wherein the eighth matrix is a column matrix comprising a difference between the second score of the third node and an average score;   determine a third matrix based on the second matrix and a second reference matrix, wherein the second reference matrix is determined based on the eighth matrix; and   determine the secondscore of the third node based on the third matrix.   
     
     
         19 . The computer device according to  claim 14 , wherein the processor is configured to:
 determine a sixth matrix based on the first node; wherein the sixth matrix comprises any one of a seventh matrix and a fourth matrix, the seventh matrix is a matrix comprising feature information of the first node, and the fourth matrix is a matrix comprising a second score of the first node; and   determine the node information of the first node based on the sixth matrix.   
     
     
         20 . A computer device, comprising:
 a processor; and   a memory arranged to store computer-executable instructions, the processor, when executing the computer-executable instructions, is configured to perform the method according to  claim 11 .